Process control systems, such as those used in the chemical processing and supply fields, typically collect a wide variety of data within a process plant environment. This data is often stored and used to identify faults and potential upcoming faults in the system. For example, the data may be examined after a process failure to identify a problem that resulted in an unusable batch of material, or to identify a component that failed or is expected to fail within a specific time frame.
Automated process control systems often are used to manage groups of sensors in a process plant. These process control systems typically monitor conditions in a process plant environment, and adjust process parameters based on conditions observed by the sensors. However, these automated systems can only take action based on the specific data available to them and specific pre-defined thresholds. When undesirable conditions result due to interactions among components and/or conditions within the process plant, or unexpected fault conditions occur, an automated system typically is unable to make an appropriate and complete response.
Thus, some supervisory monitoring of the process plant is required. This may require human operators to manage and respond to data from thousands of sources, which can be effectively infeasible. The human operator also must maintain a physical presence at the process plant to allow for prompt response to fault conditions.